The architecture of our GOHOME-Mod model is illustrated in Figure REF .
We use 1D CNN + GRU as our encoder in both agent and lane encoders to encode agent trajectories and lane centerlines. We use cross-attention block Agents2Lanes to generate agent-aware lane features and use cross-attention block Lanes2Agents to generate lane-aware agent features. We then apply self-attention block Agents2Agents to produce inter-aware agent features and use a 3-layer MLP decoder to predict the trajectory endpoints of the target agents. Finally, we use a trajectory regressor module to complete the trajectory conditioned on the predicted endpoint and the agent feature embeddings.
Similar to the other goal-based prediction approaches such as [1]}, [2]}, [3]}, [4]}, we train the trajectory regressor conditioned on the ground truth trajectory endpoints. For all attention blocks, we use multi-head attention with 8 attention heads.
